docs: def convenience_calc_fisher_approx(self, params):
          """
          Calculates the BHHH approximation of the Fisher Information Matrix for
          this model / dataset. Note that this function name is INCORRECT with
          regard to the actual actions performed. The Mixed Logit model uses a
          placeholder for the BHHH approximation of the Fisher Information Matrix
          because the BHHH approximation is already being used to approximate the
          hessian.
  
          This placeholder allows calculation of a value for the 'robust'
          standard errors, even though such a value is not useful since it is not
          correct...
          """
          shapes, intercepts, betas = self.convenience_split_params(params)
  
          placeholder_bhhh = np.diag(-1 * np.ones(betas.shape[0]))
  
          return placeholder_bhhh